{
 "cells": [
  {
   "cell_type": "raw",
   "metadata": {
    "raw_mimetype": "text/html"
   },
   "source": [
    "<style>\n",
    "pre {\n",
    " white-space: pre-wrap !important;\n",
    "}\n",
    ".table-striped > tbody > tr:nth-of-type(odd) {\n",
    "    background-color: #f9f9f9;\n",
    "}\n",
    ".table-striped > tbody > tr:nth-of-type(even) {\n",
    "    background-color: white;\n",
    "}\n",
    ".table-striped td, .table-striped th, .table-striped tr {\n",
    "    border: 1px solid black;\n",
    "    border-collapse: collapse;\n",
    "    margin: 1em 2em;\n",
    "}\n",
    ".rendered_html td, .rendered_html th {\n",
    "    text-align: left;\n",
    "    vertical-align: middle;\n",
    "    padding: 4px;\n",
    "}\n",
    "</style>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Arrow\n",
    "Vaex supports [Arrow](https://arrow.apache.org). We will demonstrate vaex+arrow by giving a quick look at a large dataset that does not fit into memory. The NYC taxi dataset for the year 2015 contains about 150 million rows containing information about taxi trips in New York, and is about 23GB in size. You can download it here:\n",
    " \n",
    " * https://docs.vaex.io/en/latest/datasets.html\n",
    "\n",
    "In case you want to convert it to the arrow format, use the code below:\n",
    "```python\n",
    "ds_hdf5 = vaex.open('/Users/maartenbreddels/datasets/nytaxi/nyc_taxi2015.hdf5')\n",
    "# this may take a while to export\n",
    "ds_hdf5.export('./nyc_taxi2015.arrow')\n",
    "```\n",
    "Also make sure you install vaex-arrow:\n",
    "```bash\n",
    "$ pip install vaex-arrow\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-rw-r--r--  1 maartenbreddels  staff    23G Oct 31 18:56 /Users/maartenbreddels/datasets/nytaxi/nyc_taxi2015.arrow\r\n"
     ]
    }
   ],
   "source": [
    "!ls -alh /Users/maartenbreddels/datasets/nytaxi/nyc_taxi2015.arrow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import vaex"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Opens instantly\n",
    "Opening the file goes instantly, since nothing is being copied to memory. The data is only memory mapped, a technique that will only read the data when needed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 3 µs, sys: 1 µs, total: 4 µs\n",
      "Wall time: 6.91 µs\n"
     ]
    }
   ],
   "source": [
    "%time\n",
    "df = vaex.open('/Users/maartenbreddels/datasets/nytaxi/nyc_taxi2015.arrow')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>.vaex-description pre {\n",
       "          max-width : 450px;\n",
       "          white-space : nowrap;\n",
       "          overflow : hidden;\n",
       "          text-overflow: ellipsis;\n",
       "        }\n",
       "\n",
       "        .vex-description pre:hover {\n",
       "          max-width : initial;\n",
       "          white-space: pre;\n",
       "        }</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table class='table-striped'><thead><tr><th>#</th><th>VendorID</th><th>dropoff_dayofweek</th><th>dropoff_hour</th><th>dropoff_latitude</th><th>dropoff_longitude</th><th>extra</th><th>fare_amount</th><th>improvement_surcharge</th><th>mta_tax</th><th>passenger_count</th><th>payment_type</th><th>pickup_dayofweek</th><th>pickup_hour</th><th>pickup_latitude</th><th>pickup_longitude</th><th>tip_amount</th><th>tolls_amount</th><th>total_amount</th><th>tpep_dropoff_datetime</th><th>tpep_pickup_datetime</th><th>trip_distance</th></tr></thead><tr><td><i style='opacity: 0.6'>0</i></td><td>2</td><td>3.0</td><td>19.0</td><td>40.75061798095703</td><td>-73.97478485107422</td><td>1.0</td><td>12.0</td><td>0.3</td><td>0.5</td><td>1</td><td>1</td><td>3.0</td><td>19.0</td><td>40.7501106262207</td><td>-73.993896484375</td><td>3.25</td><td>0.0</td><td>17.05</td><td>numpy.datetime64('2015-01-15T19:23:42.000000000')</td><td>numpy.datetime64('2015-01-15T19:05:39.000000000')</td><td>1.59</td></tr><tr><td><i style='opacity: 0.6'>1</i></td><td>1</td><td>5.0</td><td>20.0</td><td>40.75910949707031</td><td>-73.99441528320312</td><td>0.5</td><td>14.5</td><td>0.3</td><td>0.5</td><td>1</td><td>1</td><td>5.0</td><td>20.0</td><td>40.7242431640625</td><td>-74.00164794921875</td><td>2.0</td><td>0.0</td><td>17.8</td><td>numpy.datetime64('2015-01-10T20:53:28.000000000')</td><td>numpy.datetime64('2015-01-10T20:33:38.000000000')</td><td>3.3</td></tr><tr><td><i style='opacity: 0.6'>2</i></td><td>1</td><td>5.0</td><td>20.0</td><td>40.82441329956055</td><td>-73.95182037353516</td><td>0.5</td><td>9.5</td><td>0.3</td><td>0.5</td><td>1</td><td>2</td><td>5.0</td><td>20.0</td><td>40.80278778076172</td><td>-73.96334075927734</td><td>0.0</td><td>0.0</td><td>10.8</td><td>numpy.datetime64('2015-01-10T20:43:41.000000000')</td><td>numpy.datetime64('2015-01-10T20:33:38.000000000')</td><td>1.8</td></tr><tr><td><i style='opacity: 0.6'>3</i></td><td>1</td><td>5.0</td><td>20.0</td><td>40.71998596191406</td><td>-74.00432586669923</td><td>0.5</td><td>3.5</td><td>0.3</td><td>0.5</td><td>1</td><td>2</td><td>5.0</td><td>20.0</td><td>40.71381759643555</td><td>-74.00908660888672</td><td>0.0</td><td>0.0</td><td>4.8</td><td>numpy.datetime64('2015-01-10T20:35:31.000000000')</td><td>numpy.datetime64('2015-01-10T20:33:39.000000000')</td><td>0.5</td></tr><tr><td><i style='opacity: 0.6'>4</i></td><td>1</td><td>5.0</td><td>20.0</td><td>40.742652893066406</td><td>-74.00418090820312</td><td>0.5</td><td>15.0</td><td>0.3</td><td>0.5</td><td>1</td><td>2</td><td>5.0</td><td>20.0</td><td>40.762428283691406</td><td>-73.97117614746094</td><td>0.0</td><td>0.0</td><td>16.3</td><td>numpy.datetime64('2015-01-10T20:52:58.000000000')</td><td>numpy.datetime64('2015-01-10T20:33:39.000000000')</td><td>3.0</td></tr><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><tr><td><i style='opacity: 0.6'>146,112,984</i></td><td>2</td><td>4.0</td><td>0.0</td><td>40.722469329833984</td><td>-73.98621368408203</td><td>0.5</td><td>7.5</td><td>0.3</td><td>0.5</td><td>5</td><td>1</td><td>3.0</td><td>23.0</td><td>40.72087097167969</td><td>-73.99381256103516</td><td>1.76</td><td>0.0</td><td>10.56</td><td>numpy.datetime64('2016-01-01T00:08:18.000000000')</td><td>numpy.datetime64('2015-12-31T23:59:56.000000000')</td><td>1.2</td></tr><tr><td><i style='opacity: 0.6'>146,112,985</i></td><td>1</td><td>4.0</td><td>0.0</td><td>40.75238800048828</td><td>-73.93951416015625</td><td>0.5</td><td>7.5</td><td>0.3</td><td>0.5</td><td>2</td><td>2</td><td>3.0</td><td>23.0</td><td>40.76028060913085</td><td>-73.96527099609375</td><td>0.0</td><td>0.0</td><td>8.8</td><td>numpy.datetime64('2016-01-01T00:05:19.000000000')</td><td>numpy.datetime64('2015-12-31T23:59:58.000000000')</td><td>2.0</td></tr><tr><td><i style='opacity: 0.6'>146,112,986</i></td><td>1</td><td>4.0</td><td>0.0</td><td>40.69329833984375</td><td>-73.9886703491211</td><td>0.5</td><td>13.5</td><td>0.3</td><td>0.5</td><td>2</td><td>2</td><td>3.0</td><td>23.0</td><td>40.73907852172852</td><td>-73.98729705810547</td><td>0.0</td><td>0.0</td><td>14.8</td><td>numpy.datetime64('2016-01-01T00:12:55.000000000')</td><td>numpy.datetime64('2015-12-31T23:59:59.000000000')</td><td>3.8</td></tr><tr><td><i style='opacity: 0.6'>146,112,987</i></td><td>2</td><td>4.0</td><td>0.0</td><td>40.705322265625</td><td>-74.01712036132812</td><td>0.5</td><td>8.5</td><td>0.3</td><td>0.5</td><td>1</td><td>2</td><td>3.0</td><td>23.0</td><td>40.72569274902344</td><td>-73.99755859375</td><td>0.0</td><td>0.0</td><td>9.8</td><td>numpy.datetime64('2016-01-01T00:10:26.000000000')</td><td>numpy.datetime64('2015-12-31T23:59:59.000000000')</td><td>1.96</td></tr><tr><td><i style='opacity: 0.6'>146,112,988</i></td><td>2</td><td>4.0</td><td>0.0</td><td>40.76057052612305</td><td>-73.99098205566406</td><td>0.5</td><td>13.5</td><td>0.3</td><td>0.5</td><td>1</td><td>1</td><td>3.0</td><td>23.0</td><td>40.76725769042969</td><td>-73.98439788818358</td><td>2.96</td><td>0.0</td><td>17.76</td><td>numpy.datetime64('2016-01-01T00:21:30.000000000')</td><td>numpy.datetime64('2015-12-31T23:59:59.000000000')</td><td>1.06</td></tr></table>"
      ],
      "text/plain": [
       "<vaex_arrow.dataset.DatasetArrow at 0x11d87e6a0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quick viz of 146 million rows\n",
    "As can be seen, this dataset contains 146 million rows.\n",
    "Using plot, we can generate a quick overview what the data contains. The pickup locations nicely outline Manhattan."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(df.pickup_longitude, df.pickup_latitude, f='log');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-4.9630000e+02,  3.9506116e+06])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.total_amount.minmax()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data cleansing: outliers\n",
    "As can be seen from the total_amount columns (how much people payed), this dataset contains outliers. From a quick 1d plot, we can see reasonable ways to filter the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x121d26320>]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot1d(df.total_amount, shape=100, limits=[0, 100])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# filter the dataset\n",
    "dff = df[(df.total_amount >= 0) & (df.total_amount < 100)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Shallow copies\n",
    "This filtered dataset did not copy any data (otherwise it would have costed us about ~23GB of RAM). Shallow copies of the data are made instead and a booleans mask tracks which rows should be used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "dff['ratio'] = dff.tip_amount/dff.total_amount"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Virtual column\n",
    "The new column `ratio` does not do any computation yet, it only stored the expression and does not waste any memory. However, the new (virtual) column can be used in calculations as if it were a normal column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<string>:1: RuntimeWarning: invalid value encountered in true_divide\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.09601926650107262"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dff.ratio.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Result\n",
    "Our final result, the percentage of the tip, can be easily calcualted for this large dataset, it did not require any excessive amount of memory."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Interoperability\n",
    "Since the data lives as Arrow arrays, we can pass them around to other libraries such as pandas, or even pass it to other processes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pyarrow.Table\n",
       "VendorID: int64\n",
       "dropoff_dayofweek: double\n",
       "dropoff_hour: double\n",
       "dropoff_latitude: double\n",
       "dropoff_longitude: double\n",
       "extra: double\n",
       "fare_amount: double\n",
       "improvement_surcharge: double\n",
       "mta_tax: double\n",
       "passenger_count: int64\n",
       "payment_type: int64\n",
       "pickup_dayofweek: double\n",
       "pickup_hour: double\n",
       "pickup_latitude: double\n",
       "pickup_longitude: double\n",
       "tip_amount: double\n",
       "tolls_amount: double\n",
       "total_amount: double\n",
       "tpep_dropoff_datetime: timestamp[ns]\n",
       "tpep_pickup_datetime: timestamp[ns]\n",
       "trip_distance: double"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arrow_table = df.to_arrow_table()\n",
    "arrow_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>VendorID</th>\n",
       "      <th>dropoff_dayofweek</th>\n",
       "      <th>dropoff_hour</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>extra</th>\n",
       "      <th>fare_amount</th>\n",
       "      <th>improvement_surcharge</th>\n",
       "      <th>mta_tax</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>...</th>\n",
       "      <th>pickup_dayofweek</th>\n",
       "      <th>pickup_hour</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>tip_amount</th>\n",
       "      <th>tolls_amount</th>\n",
       "      <th>total_amount</th>\n",
       "      <th>tpep_dropoff_datetime</th>\n",
       "      <th>tpep_pickup_datetime</th>\n",
       "      <th>trip_distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>40.750618</td>\n",
       "      <td>-73.974785</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>40.750111</td>\n",
       "      <td>-73.993896</td>\n",
       "      <td>3.25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>17.05</td>\n",
       "      <td>2015-01-15 19:23:42</td>\n",
       "      <td>2015-01-15 19:05:39</td>\n",
       "      <td>1.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>40.759109</td>\n",
       "      <td>-73.994415</td>\n",
       "      <td>0.5</td>\n",
       "      <td>14.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>40.724243</td>\n",
       "      <td>-74.001648</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>17.80</td>\n",
       "      <td>2015-01-10 20:53:28</td>\n",
       "      <td>2015-01-10 20:33:38</td>\n",
       "      <td>3.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>40.824413</td>\n",
       "      <td>-73.951820</td>\n",
       "      <td>0.5</td>\n",
       "      <td>9.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>40.802788</td>\n",
       "      <td>-73.963341</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>10.80</td>\n",
       "      <td>2015-01-10 20:43:41</td>\n",
       "      <td>2015-01-10 20:33:38</td>\n",
       "      <td>1.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>40.719986</td>\n",
       "      <td>-74.004326</td>\n",
       "      <td>0.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>40.713818</td>\n",
       "      <td>-74.009087</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.80</td>\n",
       "      <td>2015-01-10 20:35:31</td>\n",
       "      <td>2015-01-10 20:33:39</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>40.742653</td>\n",
       "      <td>-74.004181</td>\n",
       "      <td>0.5</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>40.762428</td>\n",
       "      <td>-73.971176</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>16.30</td>\n",
       "      <td>2015-01-10 20:52:58</td>\n",
       "      <td>2015-01-10 20:33:39</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>40.758194</td>\n",
       "      <td>-73.986977</td>\n",
       "      <td>0.5</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>40.774048</td>\n",
       "      <td>-73.874374</td>\n",
       "      <td>6.70</td>\n",
       "      <td>5.33</td>\n",
       "      <td>40.33</td>\n",
       "      <td>2015-01-10 20:53:52</td>\n",
       "      <td>2015-01-10 20:33:39</td>\n",
       "      <td>9.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>40.749634</td>\n",
       "      <td>-73.992470</td>\n",
       "      <td>0.5</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>40.726009</td>\n",
       "      <td>-73.983276</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>15.30</td>\n",
       "      <td>2015-01-10 20:58:31</td>\n",
       "      <td>2015-01-10 20:33:39</td>\n",
       "      <td>2.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>40.726326</td>\n",
       "      <td>-73.995010</td>\n",
       "      <td>0.5</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>40.734142</td>\n",
       "      <td>-74.002663</td>\n",
       "      <td>1.66</td>\n",
       "      <td>0.00</td>\n",
       "      <td>9.96</td>\n",
       "      <td>2015-01-10 20:42:20</td>\n",
       "      <td>2015-01-10 20:33:39</td>\n",
       "      <td>0.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>40.759357</td>\n",
       "      <td>-73.987595</td>\n",
       "      <td>0.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>40.644356</td>\n",
       "      <td>-73.783043</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.33</td>\n",
       "      <td>58.13</td>\n",
       "      <td>2015-01-10 21:11:35</td>\n",
       "      <td>2015-01-10 20:33:39</td>\n",
       "      <td>18.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>40.759365</td>\n",
       "      <td>-73.985916</td>\n",
       "      <td>0.5</td>\n",
       "      <td>6.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>40.767948</td>\n",
       "      <td>-73.985588</td>\n",
       "      <td>1.55</td>\n",
       "      <td>0.00</td>\n",
       "      <td>9.35</td>\n",
       "      <td>2015-01-10 20:40:44</td>\n",
       "      <td>2015-01-10 20:33:40</td>\n",
       "      <td>0.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>40.728584</td>\n",
       "      <td>-74.004395</td>\n",
       "      <td>0.5</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>40.723103</td>\n",
       "      <td>-73.988617</td>\n",
       "      <td>1.66</td>\n",
       "      <td>0.00</td>\n",
       "      <td>9.96</td>\n",
       "      <td>2015-01-10 20:41:39</td>\n",
       "      <td>2015-01-10 20:33:40</td>\n",
       "      <td>0.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>40.757217</td>\n",
       "      <td>-73.967407</td>\n",
       "      <td>0.5</td>\n",
       "      <td>7.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>40.751419</td>\n",
       "      <td>-73.993782</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>9.80</td>\n",
       "      <td>2015-01-10 20:43:26</td>\n",
       "      <td>2015-01-10 20:33:41</td>\n",
       "      <td>1.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>40.707726</td>\n",
       "      <td>-74.009773</td>\n",
       "      <td>0.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>40.704376</td>\n",
       "      <td>-74.008362</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.30</td>\n",
       "      <td>2015-01-10 20:35:23</td>\n",
       "      <td>2015-01-10 20:33:41</td>\n",
       "      <td>0.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>40.735210</td>\n",
       "      <td>-73.997345</td>\n",
       "      <td>0.5</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>40.760448</td>\n",
       "      <td>-73.973946</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>23.30</td>\n",
       "      <td>2015-01-10 21:03:04</td>\n",
       "      <td>2015-01-10 20:33:41</td>\n",
       "      <td>3.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>40.739895</td>\n",
       "      <td>-73.995216</td>\n",
       "      <td>0.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>40.731777</td>\n",
       "      <td>-74.006721</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>7.30</td>\n",
       "      <td>2015-01-10 20:39:23</td>\n",
       "      <td>2015-01-10 20:33:41</td>\n",
       "      <td>1.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>40.757889</td>\n",
       "      <td>-73.983978</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>40.739811</td>\n",
       "      <td>-73.976425</td>\n",
       "      <td>4.38</td>\n",
       "      <td>0.00</td>\n",
       "      <td>22.68</td>\n",
       "      <td>2015-01-15 19:32:00</td>\n",
       "      <td>2015-01-15 19:05:39</td>\n",
       "      <td>2.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>40.786858</td>\n",
       "      <td>-73.955124</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>40.754246</td>\n",
       "      <td>-73.968704</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>14.30</td>\n",
       "      <td>2015-01-15 19:21:00</td>\n",
       "      <td>2015-01-15 19:05:40</td>\n",
       "      <td>2.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>40.785782</td>\n",
       "      <td>-73.952713</td>\n",
       "      <td>1.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>40.769581</td>\n",
       "      <td>-73.863060</td>\n",
       "      <td>8.08</td>\n",
       "      <td>5.33</td>\n",
       "      <td>41.21</td>\n",
       "      <td>2015-01-15 19:28:18</td>\n",
       "      <td>2015-01-15 19:05:40</td>\n",
       "      <td>8.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>40.786083</td>\n",
       "      <td>-73.980850</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>40.779423</td>\n",
       "      <td>-73.945541</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>13.30</td>\n",
       "      <td>2015-01-15 19:20:36</td>\n",
       "      <td>2015-01-15 19:05:41</td>\n",
       "      <td>2.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>40.718590</td>\n",
       "      <td>-73.952377</td>\n",
       "      <td>1.0</td>\n",
       "      <td>21.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>40.774010</td>\n",
       "      <td>-73.874458</td>\n",
       "      <td>4.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>27.80</td>\n",
       "      <td>2015-01-15 19:20:22</td>\n",
       "      <td>2015-01-15 19:05:41</td>\n",
       "      <td>7.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>40.714596</td>\n",
       "      <td>-73.998924</td>\n",
       "      <td>1.0</td>\n",
       "      <td>17.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>40.751896</td>\n",
       "      <td>-73.976601</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>19.30</td>\n",
       "      <td>2015-01-15 19:31:00</td>\n",
       "      <td>2015-01-15 19:05:41</td>\n",
       "      <td>3.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>40.734650</td>\n",
       "      <td>-73.999939</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>40.745079</td>\n",
       "      <td>-73.994957</td>\n",
       "      <td>1.62</td>\n",
       "      <td>0.00</td>\n",
       "      <td>8.92</td>\n",
       "      <td>2015-01-15 19:10:22</td>\n",
       "      <td>2015-01-15 19:05:41</td>\n",
       "      <td>0.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>40.735512</td>\n",
       "      <td>-74.003563</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>40.747063</td>\n",
       "      <td>-74.000938</td>\n",
       "      <td>1.30</td>\n",
       "      <td>0.00</td>\n",
       "      <td>8.60</td>\n",
       "      <td>2015-01-15 19:10:55</td>\n",
       "      <td>2015-01-15 19:05:41</td>\n",
       "      <td>0.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>40.704220</td>\n",
       "      <td>-74.007919</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>40.717892</td>\n",
       "      <td>-74.002777</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>9.80</td>\n",
       "      <td>2015-01-15 19:12:36</td>\n",
       "      <td>2015-01-15 19:05:41</td>\n",
       "      <td>1.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>40.761856</td>\n",
       "      <td>-73.978172</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>40.736362</td>\n",
       "      <td>-73.997459</td>\n",
       "      <td>2.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>15.80</td>\n",
       "      <td>2015-01-15 19:22:11</td>\n",
       "      <td>2015-01-15 19:05:41</td>\n",
       "      <td>2.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>40.811089</td>\n",
       "      <td>-73.953339</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>40.823994</td>\n",
       "      <td>-73.952278</td>\n",
       "      <td>1.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>11.00</td>\n",
       "      <td>2015-01-15 19:14:05</td>\n",
       "      <td>2015-01-15 19:05:41</td>\n",
       "      <td>1.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>40.734890</td>\n",
       "      <td>-73.988609</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>40.750080</td>\n",
       "      <td>-73.991127</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>10.80</td>\n",
       "      <td>2015-01-15 19:16:18</td>\n",
       "      <td>2015-01-15 19:05:42</td>\n",
       "      <td>1.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>40.743530</td>\n",
       "      <td>-73.985603</td>\n",
       "      <td>0.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>40.644127</td>\n",
       "      <td>-73.786575</td>\n",
       "      <td>6.00</td>\n",
       "      <td>5.33</td>\n",
       "      <td>64.13</td>\n",
       "      <td>2015-01-15 19:49:07</td>\n",
       "      <td>2015-01-15 19:05:42</td>\n",
       "      <td>18.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>40.757721</td>\n",
       "      <td>-73.994514</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>40.741447</td>\n",
       "      <td>-73.993668</td>\n",
       "      <td>2.36</td>\n",
       "      <td>0.00</td>\n",
       "      <td>14.16</td>\n",
       "      <td>2015-01-15 19:18:33</td>\n",
       "      <td>2015-01-15 19:05:42</td>\n",
       "      <td>1.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>40.704689</td>\n",
       "      <td>-74.009079</td>\n",
       "      <td>1.0</td>\n",
       "      <td>17.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>40.744083</td>\n",
       "      <td>-73.985291</td>\n",
       "      <td>3.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>23.00</td>\n",
       "      <td>2015-01-15 19:21:40</td>\n",
       "      <td>2015-01-15 19:05:42</td>\n",
       "      <td>5.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9970</th>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>40.719917</td>\n",
       "      <td>-73.955521</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>40.725979</td>\n",
       "      <td>-74.009071</td>\n",
       "      <td>4.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>24.80</td>\n",
       "      <td>2015-01-30 11:20:08</td>\n",
       "      <td>2015-01-30 10:51:40</td>\n",
       "      <td>3.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9971</th>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>40.720398</td>\n",
       "      <td>-73.984940</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>40.732452</td>\n",
       "      <td>-73.985001</td>\n",
       "      <td>1.65</td>\n",
       "      <td>0.00</td>\n",
       "      <td>9.95</td>\n",
       "      <td>2015-01-30 10:58:58</td>\n",
       "      <td>2015-01-30 10:51:40</td>\n",
       "      <td>1.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9972</th>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>40.755405</td>\n",
       "      <td>-74.002457</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>40.751358</td>\n",
       "      <td>-73.990479</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>10.30</td>\n",
       "      <td>2015-01-30 11:03:41</td>\n",
       "      <td>2015-01-30 10:51:41</td>\n",
       "      <td>0.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9973</th>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>40.763626</td>\n",
       "      <td>-73.969666</td>\n",
       "      <td>1.0</td>\n",
       "      <td>24.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>40.708790</td>\n",
       "      <td>-74.017281</td>\n",
       "      <td>5.10</td>\n",
       "      <td>0.00</td>\n",
       "      <td>31.40</td>\n",
       "      <td>2015-01-13 19:22:18</td>\n",
       "      <td>2015-01-13 18:55:41</td>\n",
       "      <td>7.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9974</th>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>40.772366</td>\n",
       "      <td>-73.960800</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>40.780003</td>\n",
       "      <td>-73.954681</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>8.30</td>\n",
       "      <td>2015-01-13 19:02:03</td>\n",
       "      <td>2015-01-13 18:55:41</td>\n",
       "      <td>0.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9975</th>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>40.733429</td>\n",
       "      <td>-73.984154</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>40.749680</td>\n",
       "      <td>-73.991531</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>10.80</td>\n",
       "      <td>2015-01-13 19:06:56</td>\n",
       "      <td>2015-01-13 18:55:41</td>\n",
       "      <td>1.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9976</th>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>40.774780</td>\n",
       "      <td>-73.957779</td>\n",
       "      <td>1.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>40.751801</td>\n",
       "      <td>-74.002327</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>23.80</td>\n",
       "      <td>2015-01-13 19:18:39</td>\n",
       "      <td>2015-01-13 18:55:42</td>\n",
       "      <td>5.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9977</th>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>40.751698</td>\n",
       "      <td>-73.989746</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>40.768433</td>\n",
       "      <td>-73.986137</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>10.30</td>\n",
       "      <td>2015-01-13 19:06:38</td>\n",
       "      <td>2015-01-13 18:55:42</td>\n",
       "      <td>1.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9978</th>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>40.752941</td>\n",
       "      <td>-73.977470</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>40.745071</td>\n",
       "      <td>-73.987068</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>10.30</td>\n",
       "      <td>2015-01-13 19:05:34</td>\n",
       "      <td>2015-01-13 18:55:42</td>\n",
       "      <td>0.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9979</th>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>40.735130</td>\n",
       "      <td>-73.976120</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>40.751259</td>\n",
       "      <td>-73.977814</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>10.30</td>\n",
       "      <td>2015-01-13 19:05:41</td>\n",
       "      <td>2015-01-13 18:55:42</td>\n",
       "      <td>1.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9980</th>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>40.745541</td>\n",
       "      <td>-73.984383</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>40.731110</td>\n",
       "      <td>-74.001350</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>10.30</td>\n",
       "      <td>2015-01-13 19:05:32</td>\n",
       "      <td>2015-01-13 18:55:42</td>\n",
       "      <td>1.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9981</th>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>40.793671</td>\n",
       "      <td>-73.974327</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>40.791222</td>\n",
       "      <td>-73.965118</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.80</td>\n",
       "      <td>2015-01-13 19:00:05</td>\n",
       "      <td>2015-01-13 18:55:42</td>\n",
       "      <td>0.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9982</th>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>40.754639</td>\n",
       "      <td>-73.986343</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>40.764175</td>\n",
       "      <td>-73.968994</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>13.80</td>\n",
       "      <td>2015-01-13 19:11:57</td>\n",
       "      <td>2015-01-13 18:55:43</td>\n",
       "      <td>1.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9983</th>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>40.723721</td>\n",
       "      <td>-73.989494</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>40.714985</td>\n",
       "      <td>-73.992409</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>8.30</td>\n",
       "      <td>2015-01-13 18:59:19</td>\n",
       "      <td>2015-01-13 18:55:43</td>\n",
       "      <td>0.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9984</th>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>40.774590</td>\n",
       "      <td>-73.963249</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>40.764881</td>\n",
       "      <td>-73.968529</td>\n",
       "      <td>1.30</td>\n",
       "      <td>0.00</td>\n",
       "      <td>8.60</td>\n",
       "      <td>2015-01-13 19:01:19</td>\n",
       "      <td>2015-01-13 18:55:44</td>\n",
       "      <td>0.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9985</th>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>40.774872</td>\n",
       "      <td>-73.982613</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>40.762344</td>\n",
       "      <td>-73.985695</td>\n",
       "      <td>1.60</td>\n",
       "      <td>0.00</td>\n",
       "      <td>10.40</td>\n",
       "      <td>2015-01-13 19:03:54</td>\n",
       "      <td>2015-01-13 18:55:44</td>\n",
       "      <td>1.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9986</th>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>40.787998</td>\n",
       "      <td>-73.953888</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>40.779526</td>\n",
       "      <td>-73.957619</td>\n",
       "      <td>1.20</td>\n",
       "      <td>0.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>2015-01-13 19:00:06</td>\n",
       "      <td>2015-01-13 18:55:44</td>\n",
       "      <td>0.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9987</th>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>40.790218</td>\n",
       "      <td>-73.975128</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>40.762226</td>\n",
       "      <td>-73.985916</td>\n",
       "      <td>2.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>15.80</td>\n",
       "      <td>2015-01-13 19:10:46</td>\n",
       "      <td>2015-01-13 18:55:44</td>\n",
       "      <td>2.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9988</th>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>40.739487</td>\n",
       "      <td>-73.989059</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>40.725056</td>\n",
       "      <td>-73.984329</td>\n",
       "      <td>2.10</td>\n",
       "      <td>0.00</td>\n",
       "      <td>13.40</td>\n",
       "      <td>2015-01-13 19:08:40</td>\n",
       "      <td>2015-01-13 18:55:44</td>\n",
       "      <td>1.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9989</th>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>40.780548</td>\n",
       "      <td>-73.959030</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>40.778542</td>\n",
       "      <td>-73.981949</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>11.30</td>\n",
       "      <td>2015-01-13 19:04:44</td>\n",
       "      <td>2015-01-13 18:55:45</td>\n",
       "      <td>1.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9990</th>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>40.761524</td>\n",
       "      <td>-73.960602</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>40.746319</td>\n",
       "      <td>-74.001114</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>16.80</td>\n",
       "      <td>2015-01-13 19:14:59</td>\n",
       "      <td>2015-01-13 18:55:45</td>\n",
       "      <td>3.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9991</th>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>40.720646</td>\n",
       "      <td>-73.989716</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>40.738167</td>\n",
       "      <td>-73.987434</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>10.80</td>\n",
       "      <td>2015-01-13 19:04:58</td>\n",
       "      <td>2015-01-13 18:55:45</td>\n",
       "      <td>1.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9992</th>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>40.795898</td>\n",
       "      <td>-73.972610</td>\n",
       "      <td>1.0</td>\n",
       "      <td>20.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>40.740582</td>\n",
       "      <td>-73.989738</td>\n",
       "      <td>4.30</td>\n",
       "      <td>0.00</td>\n",
       "      <td>26.60</td>\n",
       "      <td>2015-01-13 19:18:18</td>\n",
       "      <td>2015-01-13 18:55:45</td>\n",
       "      <td>5.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9993</th>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>40.769939</td>\n",
       "      <td>-73.981316</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>40.772015</td>\n",
       "      <td>-73.979416</td>\n",
       "      <td>1.10</td>\n",
       "      <td>0.00</td>\n",
       "      <td>7.40</td>\n",
       "      <td>2015-01-13 18:59:40</td>\n",
       "      <td>2015-01-13 18:55:45</td>\n",
       "      <td>0.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9994</th>\n",
       "      <td>2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>40.773521</td>\n",
       "      <td>-73.955353</td>\n",
       "      <td>1.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>40.713215</td>\n",
       "      <td>-74.013542</td>\n",
       "      <td>5.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>37.80</td>\n",
       "      <td>2015-01-23 18:59:52</td>\n",
       "      <td>2015-01-23 18:22:55</td>\n",
       "      <td>9.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9995</th>\n",
       "      <td>2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>40.774670</td>\n",
       "      <td>-73.947845</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>40.773186</td>\n",
       "      <td>-73.978043</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>13.30</td>\n",
       "      <td>2015-01-23 18:37:44</td>\n",
       "      <td>2015-01-23 18:22:55</td>\n",
       "      <td>2.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9996</th>\n",
       "      <td>2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>40.758148</td>\n",
       "      <td>-73.985626</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>40.752003</td>\n",
       "      <td>-73.973198</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>10.30</td>\n",
       "      <td>2015-01-23 18:34:48</td>\n",
       "      <td>2015-01-23 18:22:56</td>\n",
       "      <td>0.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9997</th>\n",
       "      <td>2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>40.768131</td>\n",
       "      <td>-73.964516</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>40.740456</td>\n",
       "      <td>-73.986252</td>\n",
       "      <td>2.46</td>\n",
       "      <td>0.00</td>\n",
       "      <td>14.76</td>\n",
       "      <td>2015-01-23 18:33:58</td>\n",
       "      <td>2015-01-23 18:22:56</td>\n",
       "      <td>2.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9998</th>\n",
       "      <td>2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>40.759171</td>\n",
       "      <td>-73.975189</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>40.770500</td>\n",
       "      <td>-73.981323</td>\n",
       "      <td>2.08</td>\n",
       "      <td>0.00</td>\n",
       "      <td>10.38</td>\n",
       "      <td>2015-01-23 18:29:22</td>\n",
       "      <td>2015-01-23 18:22:56</td>\n",
       "      <td>1.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>40.752113</td>\n",
       "      <td>-73.975189</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>40.761505</td>\n",
       "      <td>-73.968452</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.80</td>\n",
       "      <td>2015-01-23 18:27:58</td>\n",
       "      <td>2015-01-23 18:22:57</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10000 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      VendorID  dropoff_dayofweek  dropoff_hour  dropoff_latitude  \\\n",
       "0            2                3.0          19.0         40.750618   \n",
       "1            1                5.0          20.0         40.759109   \n",
       "2            1                5.0          20.0         40.824413   \n",
       "3            1                5.0          20.0         40.719986   \n",
       "4            1                5.0          20.0         40.742653   \n",
       "5            1                5.0          20.0         40.758194   \n",
       "6            1                5.0          20.0         40.749634   \n",
       "7            1                5.0          20.0         40.726326   \n",
       "8            1                5.0          21.0         40.759357   \n",
       "9            1                5.0          20.0         40.759365   \n",
       "10           1                5.0          20.0         40.728584   \n",
       "11           1                5.0          20.0         40.757217   \n",
       "12           1                5.0          20.0         40.707726   \n",
       "13           1                5.0          21.0         40.735210   \n",
       "14           1                5.0          20.0         40.739895   \n",
       "15           2                3.0          19.0         40.757889   \n",
       "16           2                3.0          19.0         40.786858   \n",
       "17           2                3.0          19.0         40.785782   \n",
       "18           2                3.0          19.0         40.786083   \n",
       "19           2                3.0          19.0         40.718590   \n",
       "20           2                3.0          19.0         40.714596   \n",
       "21           2                3.0          19.0         40.734650   \n",
       "22           2                3.0          19.0         40.735512   \n",
       "23           2                3.0          19.0         40.704220   \n",
       "24           2                3.0          19.0         40.761856   \n",
       "25           2                3.0          19.0         40.811089   \n",
       "26           2                3.0          19.0         40.734890   \n",
       "27           2                3.0          19.0         40.743530   \n",
       "28           2                3.0          19.0         40.757721   \n",
       "29           2                3.0          19.0         40.704689   \n",
       "...        ...                ...           ...               ...   \n",
       "9970         1                4.0          11.0         40.719917   \n",
       "9971         1                4.0          10.0         40.720398   \n",
       "9972         1                4.0          11.0         40.755405   \n",
       "9973         2                1.0          19.0         40.763626   \n",
       "9974         2                1.0          19.0         40.772366   \n",
       "9975         2                1.0          19.0         40.733429   \n",
       "9976         2                1.0          19.0         40.774780   \n",
       "9977         2                1.0          19.0         40.751698   \n",
       "9978         2                1.0          19.0         40.752941   \n",
       "9979         2                1.0          19.0         40.735130   \n",
       "9980         2                1.0          19.0         40.745541   \n",
       "9981         2                1.0          19.0         40.793671   \n",
       "9982         2                1.0          19.0         40.754639   \n",
       "9983         2                1.0          18.0         40.723721   \n",
       "9984         2                1.0          19.0         40.774590   \n",
       "9985         2                1.0          19.0         40.774872   \n",
       "9986         2                1.0          19.0         40.787998   \n",
       "9987         2                1.0          19.0         40.790218   \n",
       "9988         2                1.0          19.0         40.739487   \n",
       "9989         2                1.0          19.0         40.780548   \n",
       "9990         2                1.0          19.0         40.761524   \n",
       "9991         2                1.0          19.0         40.720646   \n",
       "9992         2                1.0          19.0         40.795898   \n",
       "9993         2                1.0          18.0         40.769939   \n",
       "9994         2                4.0          18.0         40.773521   \n",
       "9995         2                4.0          18.0         40.774670   \n",
       "9996         2                4.0          18.0         40.758148   \n",
       "9997         2                4.0          18.0         40.768131   \n",
       "9998         2                4.0          18.0         40.759171   \n",
       "9999         2                4.0          18.0         40.752113   \n",
       "\n",
       "      dropoff_longitude  extra  fare_amount  improvement_surcharge  mta_tax  \\\n",
       "0            -73.974785    1.0         12.0                    0.3      0.5   \n",
       "1            -73.994415    0.5         14.5                    0.3      0.5   \n",
       "2            -73.951820    0.5          9.5                    0.3      0.5   \n",
       "3            -74.004326    0.5          3.5                    0.3      0.5   \n",
       "4            -74.004181    0.5         15.0                    0.3      0.5   \n",
       "5            -73.986977    0.5         27.0                    0.3      0.5   \n",
       "6            -73.992470    0.5         14.0                    0.3      0.5   \n",
       "7            -73.995010    0.5          7.0                    0.3      0.5   \n",
       "8            -73.987595    0.0         52.0                    0.3      0.5   \n",
       "9            -73.985916    0.5          6.5                    0.3      0.5   \n",
       "10           -74.004395    0.5          7.0                    0.3      0.5   \n",
       "11           -73.967407    0.5          7.5                    0.3      0.5   \n",
       "12           -74.009773    0.5          3.0                    0.3      0.5   \n",
       "13           -73.997345    0.5         19.0                    0.3      0.5   \n",
       "14           -73.995216    0.5          6.0                    0.3      0.5   \n",
       "15           -73.983978    1.0         16.5                    0.3      0.5   \n",
       "16           -73.955124    1.0         12.5                    0.3      0.5   \n",
       "17           -73.952713    1.0         26.0                    0.3      0.5   \n",
       "18           -73.980850    1.0         11.5                    0.3      0.5   \n",
       "19           -73.952377    1.0         21.5                    0.3      0.5   \n",
       "20           -73.998924    1.0         17.5                    0.3      0.5   \n",
       "21           -73.999939    1.0          5.5                    0.3      0.5   \n",
       "22           -74.003563    1.0          5.5                    0.3      0.5   \n",
       "23           -74.007919    1.0          6.5                    0.3      0.5   \n",
       "24           -73.978172    1.0         11.5                    0.3      0.5   \n",
       "25           -73.953339    1.0          7.5                    0.3      0.5   \n",
       "26           -73.988609    1.0          9.0                    0.3      0.5   \n",
       "27           -73.985603    0.0         52.0                    0.3      0.5   \n",
       "28           -73.994514    1.0         10.0                    0.3      0.5   \n",
       "29           -74.009079    1.0         17.5                    0.3      0.5   \n",
       "...                 ...    ...          ...                    ...      ...   \n",
       "9970         -73.955521    0.0         20.0                    0.3      0.5   \n",
       "9971         -73.984940    1.0          6.5                    0.3      0.5   \n",
       "9972         -74.002457    0.0          8.5                    0.3      0.5   \n",
       "9973         -73.969666    1.0         24.5                    0.3      0.5   \n",
       "9974         -73.960800    1.0          5.5                    0.3      0.5   \n",
       "9975         -73.984154    1.0          9.0                    0.3      0.5   \n",
       "9976         -73.957779    1.0         20.0                    0.3      0.5   \n",
       "9977         -73.989746    1.0          8.5                    0.3      0.5   \n",
       "9978         -73.977470    1.0          7.5                    0.3      0.5   \n",
       "9979         -73.976120    1.0          8.5                    0.3      0.5   \n",
       "9980         -73.984383    1.0          8.5                    0.3      0.5   \n",
       "9981         -73.974327    1.0          5.0                    0.3      0.5   \n",
       "9982         -73.986343    1.0         11.0                    0.3      0.5   \n",
       "9983         -73.989494    1.0          4.5                    0.3      0.5   \n",
       "9984         -73.963249    1.0          5.5                    0.3      0.5   \n",
       "9985         -73.982613    1.0          7.0                    0.3      0.5   \n",
       "9986         -73.953888    1.0          5.0                    0.3      0.5   \n",
       "9987         -73.975128    1.0         11.5                    0.3      0.5   \n",
       "9988         -73.989059    1.0          9.5                    0.3      0.5   \n",
       "9989         -73.959030    1.0          8.5                    0.3      0.5   \n",
       "9990         -73.960602    1.0         15.0                    0.3      0.5   \n",
       "9991         -73.989716    1.0          8.0                    0.3      0.5   \n",
       "9992         -73.972610    1.0         20.5                    0.3      0.5   \n",
       "9993         -73.981316    1.0          4.5                    0.3      0.5   \n",
       "9994         -73.955353    1.0         31.0                    0.3      0.5   \n",
       "9995         -73.947845    1.0         11.5                    0.3      0.5   \n",
       "9996         -73.985626    1.0          8.5                    0.3      0.5   \n",
       "9997         -73.964516    1.0         10.5                    0.3      0.5   \n",
       "9998         -73.975189    1.0          6.5                    0.3      0.5   \n",
       "9999         -73.975189    1.0          5.0                    0.3      0.5   \n",
       "\n",
       "      passenger_count      ...       pickup_dayofweek  pickup_hour  \\\n",
       "0                   1      ...                    3.0         19.0   \n",
       "1                   1      ...                    5.0         20.0   \n",
       "2                   1      ...                    5.0         20.0   \n",
       "3                   1      ...                    5.0         20.0   \n",
       "4                   1      ...                    5.0         20.0   \n",
       "5                   1      ...                    5.0         20.0   \n",
       "6                   1      ...                    5.0         20.0   \n",
       "7                   3      ...                    5.0         20.0   \n",
       "8                   3      ...                    5.0         20.0   \n",
       "9                   2      ...                    5.0         20.0   \n",
       "10                  1      ...                    5.0         20.0   \n",
       "11                  1      ...                    5.0         20.0   \n",
       "12                  1      ...                    5.0         20.0   \n",
       "13                  1      ...                    5.0         20.0   \n",
       "14                  1      ...                    5.0         20.0   \n",
       "15                  1      ...                    3.0         19.0   \n",
       "16                  5      ...                    3.0         19.0   \n",
       "17                  5      ...                    3.0         19.0   \n",
       "18                  1      ...                    3.0         19.0   \n",
       "19                  2      ...                    3.0         19.0   \n",
       "20                  1      ...                    3.0         19.0   \n",
       "21                  1      ...                    3.0         19.0   \n",
       "22                  1      ...                    3.0         19.0   \n",
       "23                  2      ...                    3.0         19.0   \n",
       "24                  5      ...                    3.0         19.0   \n",
       "25                  5      ...                    3.0         19.0   \n",
       "26                  1      ...                    3.0         19.0   \n",
       "27                  1      ...                    3.0         19.0   \n",
       "28                  1      ...                    3.0         19.0   \n",
       "29                  6      ...                    3.0         19.0   \n",
       "...               ...      ...                    ...          ...   \n",
       "9970                1      ...                    4.0         10.0   \n",
       "9971                1      ...                    4.0         10.0   \n",
       "9972                2      ...                    4.0         10.0   \n",
       "9973                1      ...                    1.0         18.0   \n",
       "9974                5      ...                    1.0         18.0   \n",
       "9975                1      ...                    1.0         18.0   \n",
       "9976                3      ...                    1.0         18.0   \n",
       "9977                2      ...                    1.0         18.0   \n",
       "9978                1      ...                    1.0         18.0   \n",
       "9979                1      ...                    1.0         18.0   \n",
       "9980                1      ...                    1.0         18.0   \n",
       "9981                2      ...                    1.0         18.0   \n",
       "9982                1      ...                    1.0         18.0   \n",
       "9983                1      ...                    1.0         18.0   \n",
       "9984                5      ...                    1.0         18.0   \n",
       "9985                1      ...                    1.0         18.0   \n",
       "9986                2      ...                    1.0         18.0   \n",
       "9987                1      ...                    1.0         18.0   \n",
       "9988                1      ...                    1.0         18.0   \n",
       "9989                1      ...                    1.0         18.0   \n",
       "9990                1      ...                    1.0         18.0   \n",
       "9991                1      ...                    1.0         18.0   \n",
       "9992                1      ...                    1.0         18.0   \n",
       "9993                1      ...                    1.0         18.0   \n",
       "9994                1      ...                    4.0         18.0   \n",
       "9995                1      ...                    4.0         18.0   \n",
       "9996                2      ...                    4.0         18.0   \n",
       "9997                1      ...                    4.0         18.0   \n",
       "9998                3      ...                    4.0         18.0   \n",
       "9999                1      ...                    4.0         18.0   \n",
       "\n",
       "      pickup_latitude  pickup_longitude  tip_amount  tolls_amount  \\\n",
       "0           40.750111        -73.993896        3.25          0.00   \n",
       "1           40.724243        -74.001648        2.00          0.00   \n",
       "2           40.802788        -73.963341        0.00          0.00   \n",
       "3           40.713818        -74.009087        0.00          0.00   \n",
       "4           40.762428        -73.971176        0.00          0.00   \n",
       "5           40.774048        -73.874374        6.70          5.33   \n",
       "6           40.726009        -73.983276        0.00          0.00   \n",
       "7           40.734142        -74.002663        1.66          0.00   \n",
       "8           40.644356        -73.783043        0.00          5.33   \n",
       "9           40.767948        -73.985588        1.55          0.00   \n",
       "10          40.723103        -73.988617        1.66          0.00   \n",
       "11          40.751419        -73.993782        1.00          0.00   \n",
       "12          40.704376        -74.008362        0.00          0.00   \n",
       "13          40.760448        -73.973946        3.00          0.00   \n",
       "14          40.731777        -74.006721        0.00          0.00   \n",
       "15          40.739811        -73.976425        4.38          0.00   \n",
       "16          40.754246        -73.968704        0.00          0.00   \n",
       "17          40.769581        -73.863060        8.08          5.33   \n",
       "18          40.779423        -73.945541        0.00          0.00   \n",
       "19          40.774010        -73.874458        4.50          0.00   \n",
       "20          40.751896        -73.976601        0.00          0.00   \n",
       "21          40.745079        -73.994957        1.62          0.00   \n",
       "22          40.747063        -74.000938        1.30          0.00   \n",
       "23          40.717892        -74.002777        1.50          0.00   \n",
       "24          40.736362        -73.997459        2.50          0.00   \n",
       "25          40.823994        -73.952278        1.70          0.00   \n",
       "26          40.750080        -73.991127        0.00          0.00   \n",
       "27          40.644127        -73.786575        6.00          5.33   \n",
       "28          40.741447        -73.993668        2.36          0.00   \n",
       "29          40.744083        -73.985291        3.70          0.00   \n",
       "...               ...               ...         ...           ...   \n",
       "9970        40.725979        -74.009071        4.00          0.00   \n",
       "9971        40.732452        -73.985001        1.65          0.00   \n",
       "9972        40.751358        -73.990479        1.00          0.00   \n",
       "9973        40.708790        -74.017281        5.10          0.00   \n",
       "9974        40.780003        -73.954681        1.00          0.00   \n",
       "9975        40.749680        -73.991531        0.00          0.00   \n",
       "9976        40.751801        -74.002327        2.00          0.00   \n",
       "9977        40.768433        -73.986137        0.00          0.00   \n",
       "9978        40.745071        -73.987068        1.00          0.00   \n",
       "9979        40.751259        -73.977814        0.00          0.00   \n",
       "9980        40.731110        -74.001350        0.00          0.00   \n",
       "9981        40.791222        -73.965118        0.00          0.00   \n",
       "9982        40.764175        -73.968994        1.00          0.00   \n",
       "9983        40.714985        -73.992409        2.00          0.00   \n",
       "9984        40.764881        -73.968529        1.30          0.00   \n",
       "9985        40.762344        -73.985695        1.60          0.00   \n",
       "9986        40.779526        -73.957619        1.20          0.00   \n",
       "9987        40.762226        -73.985916        2.50          0.00   \n",
       "9988        40.725056        -73.984329        2.10          0.00   \n",
       "9989        40.778542        -73.981949        1.00          0.00   \n",
       "9990        40.746319        -74.001114        0.00          0.00   \n",
       "9991        40.738167        -73.987434        1.00          0.00   \n",
       "9992        40.740582        -73.989738        4.30          0.00   \n",
       "9993        40.772015        -73.979416        1.10          0.00   \n",
       "9994        40.713215        -74.013542        5.00          0.00   \n",
       "9995        40.773186        -73.978043        0.00          0.00   \n",
       "9996        40.752003        -73.973198        0.00          0.00   \n",
       "9997        40.740456        -73.986252        2.46          0.00   \n",
       "9998        40.770500        -73.981323        2.08          0.00   \n",
       "9999        40.761505        -73.968452        0.00          0.00   \n",
       "\n",
       "      total_amount  tpep_dropoff_datetime tpep_pickup_datetime trip_distance  \n",
       "0            17.05    2015-01-15 19:23:42  2015-01-15 19:05:39          1.59  \n",
       "1            17.80    2015-01-10 20:53:28  2015-01-10 20:33:38          3.30  \n",
       "2            10.80    2015-01-10 20:43:41  2015-01-10 20:33:38          1.80  \n",
       "3             4.80    2015-01-10 20:35:31  2015-01-10 20:33:39          0.50  \n",
       "4            16.30    2015-01-10 20:52:58  2015-01-10 20:33:39          3.00  \n",
       "5            40.33    2015-01-10 20:53:52  2015-01-10 20:33:39          9.00  \n",
       "6            15.30    2015-01-10 20:58:31  2015-01-10 20:33:39          2.20  \n",
       "7             9.96    2015-01-10 20:42:20  2015-01-10 20:33:39          0.80  \n",
       "8            58.13    2015-01-10 21:11:35  2015-01-10 20:33:39         18.20  \n",
       "9             9.35    2015-01-10 20:40:44  2015-01-10 20:33:40          0.90  \n",
       "10            9.96    2015-01-10 20:41:39  2015-01-10 20:33:40          0.90  \n",
       "11            9.80    2015-01-10 20:43:26  2015-01-10 20:33:41          1.10  \n",
       "12            4.30    2015-01-10 20:35:23  2015-01-10 20:33:41          0.30  \n",
       "13           23.30    2015-01-10 21:03:04  2015-01-10 20:33:41          3.10  \n",
       "14            7.30    2015-01-10 20:39:23  2015-01-10 20:33:41          1.10  \n",
       "15           22.68    2015-01-15 19:32:00  2015-01-15 19:05:39          2.38  \n",
       "16           14.30    2015-01-15 19:21:00  2015-01-15 19:05:40          2.83  \n",
       "17           41.21    2015-01-15 19:28:18  2015-01-15 19:05:40          8.33  \n",
       "18           13.30    2015-01-15 19:20:36  2015-01-15 19:05:41          2.37  \n",
       "19           27.80    2015-01-15 19:20:22  2015-01-15 19:05:41          7.13  \n",
       "20           19.30    2015-01-15 19:31:00  2015-01-15 19:05:41          3.60  \n",
       "21            8.92    2015-01-15 19:10:22  2015-01-15 19:05:41          0.89  \n",
       "22            8.60    2015-01-15 19:10:55  2015-01-15 19:05:41          0.96  \n",
       "23            9.80    2015-01-15 19:12:36  2015-01-15 19:05:41          1.25  \n",
       "24           15.80    2015-01-15 19:22:11  2015-01-15 19:05:41          2.11  \n",
       "25           11.00    2015-01-15 19:14:05  2015-01-15 19:05:41          1.15  \n",
       "26           10.80    2015-01-15 19:16:18  2015-01-15 19:05:42          1.53  \n",
       "27           64.13    2015-01-15 19:49:07  2015-01-15 19:05:42         18.06  \n",
       "28           14.16    2015-01-15 19:18:33  2015-01-15 19:05:42          1.76  \n",
       "29           23.00    2015-01-15 19:21:40  2015-01-15 19:05:42          5.19  \n",
       "...            ...                    ...                  ...           ...  \n",
       "9970         24.80    2015-01-30 11:20:08  2015-01-30 10:51:40          3.70  \n",
       "9971          9.95    2015-01-30 10:58:58  2015-01-30 10:51:40          1.10  \n",
       "9972         10.30    2015-01-30 11:03:41  2015-01-30 10:51:41          0.70  \n",
       "9973         31.40    2015-01-13 19:22:18  2015-01-13 18:55:41          7.08  \n",
       "9974          8.30    2015-01-13 19:02:03  2015-01-13 18:55:41          0.64  \n",
       "9975         10.80    2015-01-13 19:06:56  2015-01-13 18:55:41          1.67  \n",
       "9976         23.80    2015-01-13 19:18:39  2015-01-13 18:55:42          5.28  \n",
       "9977         10.30    2015-01-13 19:06:38  2015-01-13 18:55:42          1.38  \n",
       "9978         10.30    2015-01-13 19:05:34  2015-01-13 18:55:42          0.88  \n",
       "9979         10.30    2015-01-13 19:05:41  2015-01-13 18:55:42          1.58  \n",
       "9980         10.30    2015-01-13 19:05:32  2015-01-13 18:55:42          1.58  \n",
       "9981          6.80    2015-01-13 19:00:05  2015-01-13 18:55:42          0.63  \n",
       "9982         13.80    2015-01-13 19:11:57  2015-01-13 18:55:43          1.63  \n",
       "9983          8.30    2015-01-13 18:59:19  2015-01-13 18:55:43          0.70  \n",
       "9984          8.60    2015-01-13 19:01:19  2015-01-13 18:55:44          0.94  \n",
       "9985         10.40    2015-01-13 19:03:54  2015-01-13 18:55:44          1.04  \n",
       "9986          8.00    2015-01-13 19:00:06  2015-01-13 18:55:44          0.74  \n",
       "9987         15.80    2015-01-13 19:10:46  2015-01-13 18:55:44          2.19  \n",
       "9988         13.40    2015-01-13 19:08:40  2015-01-13 18:55:44          1.48  \n",
       "9989         11.30    2015-01-13 19:04:44  2015-01-13 18:55:45          1.83  \n",
       "9990         16.80    2015-01-13 19:14:59  2015-01-13 18:55:45          3.27  \n",
       "9991         10.80    2015-01-13 19:04:58  2015-01-13 18:55:45          1.56  \n",
       "9992         26.60    2015-01-13 19:18:18  2015-01-13 18:55:45          5.40  \n",
       "9993          7.40    2015-01-13 18:59:40  2015-01-13 18:55:45          0.34  \n",
       "9994         37.80    2015-01-23 18:59:52  2015-01-23 18:22:55          9.05  \n",
       "9995         13.30    2015-01-23 18:37:44  2015-01-23 18:22:55          2.32  \n",
       "9996         10.30    2015-01-23 18:34:48  2015-01-23 18:22:56          0.92  \n",
       "9997         14.76    2015-01-23 18:33:58  2015-01-23 18:22:56          2.36  \n",
       "9998         10.38    2015-01-23 18:29:22  2015-01-23 18:22:56          1.05  \n",
       "9999          6.80    2015-01-23 18:27:58  2015-01-23 18:22:57          0.75  \n",
       "\n",
       "[10000 rows x 21 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Although you can 'convert' (pass the data) in to pandas,\n",
    "# some memory will be wasted (at least an index will be created by pandas)\n",
    "# here we just pass a subset of the data\n",
    "df_pandas = df[:10000].to_pandas_df()\n",
    "df_pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tutorial\n",
    "If you want to learn more on vaex, take a look at the [tutorials to see what is possible](https://docs.vaex.io/en/latest/tutorial.html)."
   ]
  }
 ],
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